As a classical sparse reconstruction algorithm, iterative soft thresholding (IST) is often used in SAR sparse imaging applications. However, for moving targets, the radar echo contains an unknown phase error, so it is difficult to directly apply the IST algorithm to reconstruct the SAR moving target image. In this article, a parametric IST (P-IST) algorithm is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In this algorithm, the echo phase error is modeled as a function of the phase compensation factor, and in the iteration process, a phase compensation factor optimization selection step based on residual energy minimization is added. Compared with the existing algorithms, the proposed P-IST algorithm can obtain the global optimal solution and has better efficiency and robustness. Both simulated data and measured data are used to validate the effectiveness of the proposed algorithm.

中文翻译：

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SAR图像中运动目标重聚焦的参数化迭代软阈值算法


迭代软阈值（IST）作为经典的稀疏重建算法，常用于SAR稀疏成像应用中。然而，对于运动目标，雷达回波包含未知的相位误差，因此很难直接应用IST算法来重建SAR运动目标图像。在本文中，提出了一种参数化 IST (P-IST) 算法，用于对合成孔径雷达 (SAR) 图像中的移动目标进行重新聚焦。该算法将回波相位误差建模为相位补偿因子的函数，并且在迭代过程中增加了基于残余能量最小化的相位补偿因子优化选择步骤。与现有算法相比，所提出的P-IST算法可以获得全局最优解，并且具有更好的效率和鲁棒性。仿真数据和实测数据均用于验证所提算法的有效性。